AdaDiff: Accelerating Diffusion Models through Step-Wise Adaptive Computation

Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. pp. 248–255. Ieee (2009)

2009

Earlier work this paper cites.

Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)

2009

Earlier work this paper cites.

Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13. pp. 740–755. Springer (2014)

2014

Earlier work this paper cites.

Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of International Conference on Computer Vision (ICCV) (December 2015)

2015

Earlier work this paper cites.

Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. pp. 234–241. Springer (2015)

2015

Earlier work this paper cites.

Teerapittayanon, S., McDanel, B., Kung, H.T.: Branchynet: Fast inference via early exiting from deep neural networks. In: 2016 23rd International Conference on Pattern Recognition (ICPR). pp. 2464–2469. IEEE (2016)

2016

Earlier work this paper cites.

Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)

Original

2017

Earlier work this paper cites.

Brock, A., Donahue, J., Simonyan, K.: Large scale gan training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096 (2018)

Original

2018

Earlier work this paper cites.

Xu, T., Zhang, P., Huang, Q., Zhang, H., Gan, Z., Huang, X., He, X.: Attngan: Fine-grained text to image generation with attentional generative adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1316–1324 (2018)

2018

Earlier work this paper cites.

Song, Y., Ermon, S.: Generative modeling by estimating gradients of the data distribution. Advances in neural information processing systems 32

2019

Earlier work this paper cites.

Zhu, M., Pan, P., Chen, W., Yang, Y.: Dm-gan: Dynamic memory generative adversarial networks for text-to-image synthesis. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 5802–5810 (2019)

2019

Earlier work this paper cites.

Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)

Original

2020

Earlier work this paper cites.

Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems 33

2020

Earlier work this paper cites.

Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020)

Original

2020

Earlier work this paper cites.

Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456 (2020)

Original

2020

Earlier work this paper cites.

Xin, J., Tang, R., Lee, J., Yu, Y., Lin, J.: Deebert: Dynamic early exiting for accelerating bert inference. arXiv preprint arXiv:2004.12993 (2020)

Original

2020

Earlier work this paper cites.

Austin, J., Johnson, D.D., Ho, J., Tarlow, D., van den Berg, R.: Structured denoising diffusion models in discrete state-spaces. Advances in Neural Information Processing Systems 34

2021

Earlier work this paper cites.

Dhariwal, P., Nichol, A.: Diffusion models beat gans on image synthesis. Advances in Neural Information Processing Systems 34

2021

Cited alongside, same era.

Jolicoeur-Martineau, A., Li, K., Piché-Taillefer, R., Kachman, T., Mitliagkas, I.: Gotta go fast when generating data with score-based models. arXiv preprint arXiv:2105.14080 (2021)

Original

2021

Cited alongside, same era.

Kim, D., Shin, S., Song, K., Kang, W., Moon, I.C.: Soft truncation: A universal training technique of score-based diffusion model for high precision score estimation. arXiv preprint arXiv:2106.05527 (2021)

Original

2021

Cited alongside, same era.

Luhman, E., Luhman, T.: Knowledge distillation in iterative generative models for improved sampling speed. arXiv preprint arXiv:2101.02388 (2021)

Original

2021

Cited alongside, same era.

San-Roman, R., Nachmani, E., Wolf, L.: Noise estimation for generative diffusion models. arXiv preprint arXiv:2104.02600 (2021)

Lugmayr, A., Danelljan, M., Romero, A., Yu, F., Timofte, R., Van Gool, L.: Repaint: Inpainting using denoising diffusion probabilistic models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11461–11471 (2022)

2022

Later among the works it cites.

Lyu, Z., Xu, X., Yang, C., Lin, D., Dai, B.: Accelerating diffusion models via early stop of the diffusion process. arXiv preprint arXiv:2205.12524 (2022)

Original

2022

Later among the works it cites.

Meng, C., Gao, R., Kingma, D.P., Ermon, S., Ho, J., Salimans, T.: On distillation of guided diffusion models. arXiv preprint arXiv:2210.03142 (2022)

Original

2022

Later among the works it cites.

Peebles, W., Xie, S.: Scalable diffusion models with transformers. arXiv preprint arXiv:2212.09748 (2022)

Original

2022

Later among the works it cites.